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• The Colab Team

Why Your AI Pilot Worked But You Can't Scale It

#AI implementation#scaling#SME#strategy

Here’s a statistic that should concern every business leader: 88% of New Zealand organisations using AI haven’t managed to scale it beyond pilots or departmental use.

Only 12% have achieved organisation-wide implementation.

This isn’t a technology problem. The AI works. The pilots succeed. But something breaks when it’s time to scale.

After helping dozens of NZ businesses navigate this transition, we’ve identified the patterns that separate the 12% from the rest.

The pilot trap

Pilots are seductive. They’re low-risk, contained, and often produce impressive results. A marketing team automates their email campaigns. A customer service team deploys a chatbot. Finance uses AI to categorise expenses.

Each pilot works. Each team is happy. Leadership sees the demos and gets excited.

Then nothing happens.

The pilot becomes permanent. It stays in one team, solving one problem, while the rest of the organisation watches from the sidelines.

Why scaling is fundamentally different

Moving from pilot to production requires solving problems that don’t exist at small scale:

1. Data integration

Your pilot probably worked with clean, contained data. Scaling means connecting systems that were never designed to talk to each other.

The 2025 State of AI Index found that 22% of organisations cite data quality or integration issues as their primary barrier.

2. Skills distribution

In a pilot, you can rely on one or two enthusiasts who figured out the tool. At scale, you need capability across the organisation.

32% of NZ businesses cite lack of internal skills as their main barrier to AI adoption. This is the biggest single blocker.

3. Governance and trust

A pilot can run on informal approval. Organisation-wide deployment needs:

  • Clear policies on data usage
  • Defined accountability for AI decisions
  • Processes for monitoring and correction

Currently, less than 10% of NZ organisations have mature AI governance frameworks.

4. Shadow AI

When official AI rollout is slow, people find workarounds. 52% of leaders identify “shadow AI” — unapproved tools being used without oversight — as a problem.

This creates security risks, data privacy issues, and inconsistent quality.

What the 12% do differently

The organisations that successfully scale AI share common characteristics:

They start with the problem, not the technology

Failed scaling often starts with: “We should use AI for something.”

Successful scaling starts with: “This specific business process costs us $X and takes Y hours. How can we improve it?”

They invest in capability building

The skills gap doesn’t close itself. Successful organisations:

  • Train existing staff rather than just hiring specialists
  • Create internal communities of practice
  • Document what works so knowledge spreads

They establish governance early

Not bureaucracy — just clarity about:

  • Who can deploy AI tools
  • What data can be used
  • How decisions will be reviewed
  • Who’s accountable when things go wrong

They plan for integration from day one

The question isn’t “does this AI tool work?” It’s “does this AI tool work with our ERP, our CRM, our data warehouse, and our existing workflows?”

They measure business outcomes, not AI metrics

Model accuracy is interesting. Revenue impact, cost reduction, and time saved are what matter.

The practical path from pilot to production

Based on what we’ve seen work:

Phase 1: Audit your current state

  • What AI tools are already in use (including shadow AI)?
  • What pilots have succeeded? Why haven’t they scaled?
  • Where are the integration challenges?

Phase 2: Pick one high-impact process

  • Choose something with clear business metrics
  • Ensure data is accessible and clean enough
  • Get executive sponsorship (not just approval)

Phase 3: Build the capability alongside the technology

  • Train the team who’ll own it
  • Document everything
  • Create feedback loops

Phase 4: Establish lightweight governance

  • Define who can do what
  • Set up monitoring
  • Plan for when things go wrong

Phase 5: Measure ruthlessly

  • Track business outcomes, not activity
  • Compare against the baseline you established
  • Be honest about what’s working

The cost of staying stuck

Every month you spend in pilot mode, your competitors are:

  • Learning what works
  • Building organisational capability
  • Capturing productivity gains
  • Moving further ahead

The AI Forum NZ research shows that businesses successfully using AI report:

  • 91% efficiency improvements
  • 77% operational cost reductions
  • Over $50,000 annual benefits for more than a quarter of adopters

These gains compound. Starting later means catching up is harder.

Getting unstuck

If you recognise your organisation in this article — successful pilots but stalled scaling — you’re in good company. Most NZ businesses are in the same position.

The good news: the path forward is well-understood. It’s not about better technology. It’s about:

  • Building skills across your organisation
  • Solving integration challenges
  • Establishing governance that enables rather than blocks
  • Focusing on business outcomes

The 12% who’ve scaled aren’t smarter or better resourced. They just approached the problem differently.

Stuck between pilot and production? We can help. Our discovery workshops are designed to identify exactly what’s blocking your AI scaling and create a practical path forward.

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